APPLIED REGRESSION AND TIME SERIES PREDICTIVE MODELING
Session
Regular Academic Session
Class Number
6274
Career
Undergraduate
Units
4 units
Grading
UNDERGRADUATE GRADING
Description
Simple and multiple regression, least squares estimates, hypothesis testing, confidence intervals and prediction intervals, model building methods and diagnostic checking. Non-seasonal time series models: autoregressive, moving-average and/or autoregressive integrated moving-average models, parameter estimation and forecasting. Minitab or a similar software is used for real data analysis. Prerequisites: MATH 265 or equivalent and MATH 332/ MATH 532 or equivalent.
Enrollment Requirements
MATH 337 requires a grade of C (2.0) or higher in MATH 265 and MATH 332. MATH 337 requires a grade of C (2.0) or higher in MATH 265 and MATH 332.
Class Details
Instructor(s)
Mostafa S. Aminzadeh
Meets
MoWe 10:00AM - 11:50AM
Dates
08/26/2019 - 12/17/2019
Room
YR0216
Campus
Main Academic Campus
Location
On Campus
Components
Lecture Required
Class Availability
Status
Open
Seats Taken
17
Seats Open
11
Combined Section Capacity
28
Wait List Total
0
Wait List Capacity
0
Combined Section
APPL REGRESS TIME SERIES MODEL
MATH 533 - 001 (5660)
Status: Open
Seats Taken: 2
Wait List Total: 0
APPL REGRESS TIME SERIES MODEL
MATH 337 - 001 (6274)
Status: Open
Seats Taken: 15
Wait List Total: 0